Ensemble Learning for Stellar Classification and Radius Estimation from Multimodal Data  

作　　者：Zhi-Jie Deng Sheng-Yuan Yu A-Li Luo Xiao Kong Xiang-Ru Li 

机构地区：[1]School of Computer Science,South China Normal University,Guangzhou 510631,China [2]School of Computer Science and Technology,Harbin Institute of Technology,Shenzhen 518055,China [3]CAS Key Laboratory of Optical Astronomy,National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China [4]School of Astronomy and Space Science,University of Chinese Academy of Sciences,Beijing 101408,China

出　　处：《Research in Astronomy and Astrophysics》2024年第11期211-224,共14页天文和天体物理学研究（英文版）

基　　金：supported by the National Natural Science Foundation of China(12261141689,12273075,and 12373108);the National Key R&D Program of China No.2019YFA0405502;the science research grants from the China Manned Space Project with No.CMS-CSST-2021-B05。

摘　　要：Stellar classification and radius estimation are crucial for understanding the structure of the Universe and stella evolution.With the advent of the era of astronomical big data,multimodal data are available and theoretically effective for stellar classification and radius estimation.A problem is how to improve the performance of this task by jointly using the multimodal data.However,existing research primarily focuses on using single-modal data.To this end,this paper proposes a model,Multi-Modal SCNet,and its ensemble model Multimodal Ensemble fo Stellar Classification and Regression(MESCR)for improving stellar classification and radius estimation performance by fusing two modality data.In this problem,a typical phenomenon is that the sample numbers o some types of stars are evidently more than others.This imbalance has negative effects on model performance Therefore,this work utilizes a weighted sampling strategy to deal with the imbalance issues in MESCR.Som evaluation experiments are conducted on a test set for MESCR and the classification accuracy is 96.1%,and th radius estimation performance Mean of Absolute Error andσare 0.084 dex and 0.149 R_(⊙),respectively.Moreover we assessed the uncertainty of model predictions,confirming good consistency within a reasonable deviation range.Finally,we applied our model to 50,871,534 SDSS stars without spectra and published a new catalog.

关 键 词：METHODS data analysis TECHNIQUES image processing METHODS STATISTICAL 

分 类 号：P152[天文地球—天文学]